Support vector channel selection in BCI

By T. N. Lal, Michael W. Tangermann, Thilo Hinterberger, Jason Aaron Edward Weston, Martin Bogdan, Niels Birbaumer, Bernhard Schölkopf. IEEE Transactions on Biomedical Engineering, 2004

548 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3c32b60e2797be3351f6bc72f0ffcd9eb4439a6b

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Citations in the corpus

Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI (2011, methodology): “The SVM classiﬁer was used in the classiﬁcation step for all the mentioned channel selection methods.”
A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update (2018, methodology): “Let us also mention the support vector machine for channel selection [115], linear regressor for knowledge extraction [123], genetic algorithms for spectral feature selection [50] and P300-based feature selection [201], or evolutionary algorithms for feature selection based on multiresolution analys”
A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals (2007, methodology): “Feature selection/dimensionality reduction method Reference ID Genetic algorithm (GA) (Flotzinger et al 1994, Garrett et al 2003, Graimann et al 2003a, 2004, Peterson et al 2005, Scherer et al 2004, Schroder et al 2003, Tavakolian et al 2004, Yom-Tov and Inbar 2001, 2002) Principal component analysi”
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs (2006, methodology): “posed and utilized in BCI research stemming from recursive feature elimination (RFE) or genetic algorithms [17]–[21].”
Toward enhanced P300 speller performance. (2008, unknown): “Alternative feature extraction methods in a BCI context are given in [12][14].”
BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller (2008, background): “to adapt to the mental activity of the subject, leading then to better performances [ 9 ].”
EEG feature extraction based on wavelet packet decomposition for brain computer interface (2008, background): “) to produce distinguishable electroencephalogram (EEG) signals [8,9].”
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns (2008, unknown): “SLR could be used to determine relevant channels (Lal et al., 2004) or frequency bands in advance, and thus may provide a tool to customize the input features to BCI for individual subjects.”
A review of channel selection algorithms for EEG signal processing (2015, methodology): “Lal et al. [40] adopted feature selection algorithms, recursive feature elimination (RFE), and zero-norm optimization based on the training of SVMs for channel selection and demonstrated the usefulness of these operations on motor imagery classification.”
Introduction to machine learning for brain imaging (2011, unknown): “…al., 2001, 2003; Parra et al., 2002, 2003, 2008; Lal et al., 2004; Blankertz et al., 2006a, 2008b; Müller et al., 2008; Tomioka et al., 2007; Tomioka and Müller, 2010), (c) spatio-temporal ﬁ ltering algorithms (e.g. Fukunaga, 1972; Koles, 1991; Ramoser et al., 2000; Lemm et al., 2005;…”
Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. (2006, methodology): “Figure 4 shows the perfect nonoverlapping classification of hand and tongue movements at the sensorimotor cortex ( Support Vector Ma - chines , SVM , were used as classification algorithms ; see Lal et al .”